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2014 IEEE International Conference on Data Mining Workshop (ICDMW) (2014)
Shenzhen, China
Dec. 14, 2014 to Dec. 14, 2014
ISBN: 978-1-4799-4275-6
pp: 1219-1222
Clinical pathways define the essential component of the complex care process, with the objective to optimize patient outcomes and resource allocation. Clinical pathway analysis has gained increased attention in order to augment the patient treatment process. In this demonstration paper, we propose Pathway-Finder, an interactive recommender system to visually explore and discover clinical pathways. The interactive web service efficiently collects and displays patient information in a meaningful way to support an effective personalized treatment plan. Pathway-Finder implements a Bayesian Network to discover causal relationships among different factors. To support real-time recommendation and visualization, a key-value structure has been implemented to traverse the Bayesian Network faster. Additionally, Pathway-Finder is a cloud based web service hosted on Microsoft Azure which enables the health providers to access the system without the need to deploy analytics infrastructure. We demonstrate Pathway-Finder to interactively recommend personalized interventions to minimize 30-day readmission risk for Heart Failure (HF).
Bayes methods, Heart, Probability distribution, Real-time systems, Medical treatment, Diseases, Algorithm design and analysis

R. Liu et al., "Pathway-Finder: An Interactive Recommender System for Supporting Personalized Care Pathways," 2014 IEEE International Conference on Data Mining Workshop (ICDMW), Shenzhen, China, 2014, pp. 1219-1222.
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